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SPLIT PROBE DETECTION OF THE INFLUENZA A VIRUS FOR IMPROVED DIAGNOSTICS IN A POINT OF CARE SYSTEM
Title
SPLIT PROBE DETECTION OF THE INFLUENZA A VIRUS FOR IMPROVED DIAGNOSTICS IN A POINT OF CARE SYSTEM.
Creator
Yishay, Tamar, Gerasimova, Yulia, Harper, James, University of Central Florida
Abstract / Description
A group of Influenza viruses, RNA containing viruses of the Orthomyxoviridae family, consists of Influenza virus types A-D and has been known to cause the Flu, a respiratory illness associated with numerous detrimental symptoms that can lead to death. Influenza A virus (IAV) is constantly changing and is capable of causing pandemics. Currently used diagnostic methods include virus culturing, immunoassays including rapid influenza detection tests (RIDTs), and molecular assays including those...
Show moreA group of Influenza viruses, RNA containing viruses of the Orthomyxoviridae family, consists of Influenza virus types A-D and has been known to cause the Flu, a respiratory illness associated with numerous detrimental symptoms that can lead to death. Influenza A virus (IAV) is constantly changing and is capable of causing pandemics. Currently used diagnostic methods include virus culturing, immunoassays including rapid influenza detection tests (RIDTs), and molecular assays including those based on RT-PCR. Most of the methods can be only performed in the certified diagnostic laboratories equipped with sophisticated instrumentation and/or special biosafety facilities. The results using these methods are not available on a timely basis. RIDTs provide response within 15 minutes but are unable to differentiate between the IAV subtypes. New diagnostic technique, which allows reliable detection of the influenza virus infection and virus genotyping at point-of-care setting, are needed to prevent the spread of the virus and the occurrence of a pandemic. In this project, we propose to use split G-quadruplex (G4) peroxidase probes targeting a fragment of the IAV genome amplified using an isothermal RNA amplification reaction for the detection of IAV infection and virus genotyping. The probes selectively report the virus RNA target with a color change, which can be read by the naked eye. They are capable of differentiating the targets containing as little as a single-nucleotide variation in their sequences. This study aims to optimize the probes, test their selectivity, and calculate the detection limit.
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Date Issued
2019
Identifier
CFH2000533, ucf:45639
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFH2000533
DETECTING MALICIOUS SOFTWARE BY DYNAMICEXECUTION
Title
DETECTING MALICIOUS SOFTWARE BY DYNAMICEXECUTION.
Creator
Dai, Jianyong, Guha, Ratan, University of Central Florida
Abstract / Description
Traditional way to detect malicious software is based on signature matching. However, signature matching only detects known malicious software. In order to detect unknown malicious software, it is necessary to analyze the software for its impact on the system when the software is executed. In one approach, the software code can be statically analyzed for any malicious patterns. Another approach is to execute the program and determine the nature of the program dynamically. Since the execution...
Show moreTraditional way to detect malicious software is based on signature matching. However, signature matching only detects known malicious software. In order to detect unknown malicious software, it is necessary to analyze the software for its impact on the system when the software is executed. In one approach, the software code can be statically analyzed for any malicious patterns. Another approach is to execute the program and determine the nature of the program dynamically. Since the execution of malicious code may have negative impact on the system, the code must be executed in a controlled environment. For that purpose, we have developed a sandbox to protect the system. Potential malicious behavior is intercepted by hooking Win32 system calls. Using the developed sandbox, we detect unknown virus using dynamic instruction sequences mining techniques. By collecting runtime instruction sequences in basic blocks, we extract instruction sequence patterns based on instruction associations. We build classification models with these patterns. By applying this classification model, we predict the nature of an unknown program. We compare our approach with several other approaches such as simple heuristics, NGram and static instruction sequences. We have also developed a method to identify a family of malicious software utilizing the system call trace. We construct a structural system call diagram from captured dynamic system call traces. We generate smart system call signature using profile hidden Markov model (PHMM) based on modularized system call block. Smart system call signature weakly identifies a family of malicious software.
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Date Issued
2009
Identifier
CFE0002798, ucf:48141
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002798